User modeling
Single prototype Matrix factorization Distributed representation Variational auto-encoders: * "Variational autoencoders for collaborative filtering": Liang et al. (2018)Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. Varia- tional autoencoders for collaborative filtering. arXiv preprint arXiv:1802.05814, 2018. * "queriable" variant that takes into account the ambiguity of people who have many interests (but still use a single Gaussian distribution to model them): Wu et al. (2019)Wu, G., Bouadjenek, M. R., & Sanner, S. (2019). One-Class Collaborative Filtering with the Queryable Variational Autoencoder. Multiple prototypes/clusters Wu et al. (2019)Wu, G., Bouadjenek, M. R., & Sanner, S. (2019). One-Class Collaborative Filtering with the Queryable Variational Autoencoder. demonstrate that users who interact with more items often have diverse interests and modeling these interests properly help improve the relevance of recommendations. Content-based From Wang et al. (2006)Wang, J., Li, Z., Yao, J., Sun, Z., Li, M., & Ma, W. Y. (2006, January). Adaptive user profile model and collaborative filtering for personalized news. In Asia-Pacific Web Conference (pp. 474-485). Springer, Berlin, Heidelberg.: Chen et al. (2001) and Pretschener et al. (1999) propose "a profile model with hierarchical concept categories"C. C. Chen, M. Chen, and Y. Sun. PVA: A Self-Adaptive Personal View Agent. In Proceed- ings of the Seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001 A. Pretschner, and S. Gauch. Ontology Based Personalized Search. In 11th IEEE Intl. Conf. On Tools with Artificial Intelligence, 1999 Collaborative filtering Discrete presentation From Si and Jin (2003)Si, L., & Jin, R. (2003). Flexible mixture model for collaborative filtering. In Proceedings of the 20th International Conference on Machine Learning (ICML-03) (pp. 704-711).: * "The aspect model (Hofmann & Puzicha, 1999)Hofmann, T., & Puzicha, J. (1999). Latent Class Models for Collaborative Filtering. In the Proceedings of International Joint Conference on Artificial Intelligence. is a probabilistic latent space model, which models individual preferences as a convex combination of preference factors. ... The aspect model assumes that users and items are independent from each other given the latent class variable."Hofmann, T., & Puzicha, J. (1999). Latent Class Models for Collaborative Filtering. In the Proceedings of International Joint Conference on Artificial Intelligence. * "two-sided clustering model is proposed for collaborative filtering in (Hofmann & Puzicha, 1999)Hofmann, T., & Puzicha, J. (1999). Latent Class Models for Collaborative Filtering. In the Proceedings of International Joint Conference on Artificial Intelligence. . This model assumes that each user should belong to exactly one group of users and the same is true for each item" * "In the personality diagnosis model (Pennock et al., 2000)Pennock, D. M., Horvitz, E., Lawrence, S., & Giles, C. L. (2000). Collaborative Filtering by Personality Diagosis: A Hybrid Memory- and Model-Based Approach. In the Proceeding of the Sixteenth Conference on Uncertainty in Artificial Intelligence. , the observed rating for the test user yt on an item x is assumed to be drawn from an independent normal distribution with the mean as the true rating" Si and Jin (2003): "FMM extends existing partitioning/clustering algorithms for collaborative filtering by clustering both users and items together simultaneously without assuming that each user and item should only belong to a single cluster" See also * Kumar Bhargav Srinivasan's survey 2018 References Category:Contents